{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "%matplotlib inline\n",
    "\n",
    "import skimage.io\n",
    "\n",
    "show = skimage.io.imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Main script for FCNT tracker. \n",
    "\"\"\"\n",
    "#%%\n",
    "# Import custom class and functions\n",
    "from inputproducer import InputProducer\n",
    "from tracker import TrackerVanilla\n",
    "from vgg16 import Vgg16\n",
    "from selcnn import SelCNN\n",
    "from sgnet import GNet, SNet\n",
    "from utils import img_with_bbox, IOU_eval\n",
    "\n",
    "import numpy as np \n",
    "import tensorflow as tf\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "import os\n",
    "import time\n",
    "#%%\n",
    "tf.app.flags.DEFINE_integer('iter_step_sel', 600,\n",
    "                          \"\"\"Number of steps for trainning\"\"\"\n",
    "                          \"\"\"selCNN networks.\"\"\")\n",
    "tf.app.flags.DEFINE_integer('iter_step_sg', 50,\n",
    "                          \"\"\"Number of steps for trainning\"\"\"\n",
    "                          \"\"\"SGnet works\"\"\")\n",
    "tf.app.flags.DEFINE_integer('num_sel', 512,\n",
    "                          \"\"\"Number of feature maps selected.\"\"\")\n",
    "tf.app.flags.DEFINE_integer('iter_max', 200,\n",
    "\t\t\t\t\t\t\t\"\"\"Max iter times through imgs\"\"\")\n",
    "\n",
    "FLAGS = tf.app.flags.FLAGS\n",
    "\n",
    "## Define varies path\n",
    "DATA_ROOT = 'data/Dog1'\n",
    "PRE_ROOT = os.path.join(DATA_ROOT, 'img_loc')\n",
    "IMG_PATH = os.path.join(DATA_ROOT, 'img')\n",
    "GT_PATH = os.path.join(DATA_ROOT, 'groundtruth_rect.txt')\n",
    "VGG_WEIGHTS_PATH = 'vgg16_weights.npz'\n",
    "#%%\n",
    "\n",
    "\n",
    "\n",
    "def train_sgNet(sess, gnet, snet, sgt_M, ggt_M, feed_dict):\n",
    "\t\"\"\"\n",
    "\tTrain sgnet by minimize the loss\n",
    "\tLoss = Lg + Ls\n",
    "\twhere Li = |pre_Mi - gt_M|**2 + Weights_decay_term_i\n",
    "\n",
    "\t\"\"\"\n",
    "\t# Initialize sgNet variables\n",
    "\tsgNet_vars = gnet.variables + snet.variables\n",
    "\tinit_SGNet_vars_op = tf.initialize_variables(sgNet_vars, name='init_sgNet')\n",
    "\tsess.run(init_SGNet_vars_op)\n",
    "\n",
    "\t# Define composite loss\n",
    "\ttotal_losses = snet.loss(sgt_M) + gnet.loss(ggt_M)\n",
    "\n",
    "\t# Define trainning op\n",
    "\toptimizer = tf.train.GradientDescentOptimizer(1e-6)\n",
    "\ttrain_op = optimizer.minimize(total_losses, var_list= sgNet_vars)\n",
    "\n",
    "\tfor step in range(FLAGS.iter_step_sg):\n",
    "\t\tloss, _ = sess.run([total_losses, train_op], feed_dict = feed_dict)\n",
    "\t\tprint(\"SGNet Loss: \", loss)\n",
    "\n",
    "\n",
    "\n",
    "def gen_mask_phi(img_sz, loc):\n",
    "\tx,y,w,h = loc\n",
    "\tphi = np.zeros(img_sz)\n",
    "\tphi[y-int(0.5*h): y+int(0.5*h), x-int(0.5*w):x+int(0.5*w)] = 1\n",
    "\treturn phi\n",
    "#%%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from utils import variable_on_cpu, variable_with_weight_decay\n",
    "\n",
    "def non2zero(l):\n",
    "\treturn [0 if i==None else i for i in l] \n",
    "\n",
    "class SelCNN:\n",
    "\tdef __init__(self, scope, vgg_conv_layer, gt_M_sz):\n",
    "\t\t\"\"\"\n",
    "\t\tselCNN network class. Initialize graph.\n",
    "\n",
    "\t\tArgs: \n",
    "\t\t\tname: string, name of the network.\n",
    "\t\t\tvgg_conv_layer: tensor, either conv4_3 or cnv5_3 layer\n",
    "\t\t\t\tof a pretrained vgg16 network\n",
    "\t\t\"\"\"\n",
    "\t\t# Initialize network\n",
    "\t\tself.scope = scope\n",
    "\t\tself.input_layer = vgg_conv_layer\n",
    "\n",
    "\t\t# Unpack vgg_conv_layer for partial derivatives\n",
    "\t\tself.feature_maps = [vgg_conv_layer[...,i] for i in range(512)]\n",
    "\t\tself.variables = []\n",
    "\t\tself.params = {\n",
    "\t\t'dropout_rate': 0.3,\n",
    "\t\t'k_size': [3, 3, 512, 1],\n",
    "\t\t'wd': 0.0,\n",
    "\t\t'lr_initial': 1e-6, # 1e-8 gives 438 after 200 steps, 1e-7 gives better maps?\n",
    "\t\t'lr_decay_steps': 500,\n",
    "\t\t'lr_decay_rate':  0.5\n",
    "\t\t}\n",
    "\t\twith tf.name_scope(scope) as scope:\n",
    "\t\t\tself.pre_M = self._get_pre_M()\n",
    "\t\t\tself.gt_M = tf.placeholder(tf.float32, shape=gt_M_sz)\n",
    "\t\tself.pre_M_size = self.pre_M.get_shape().as_list()[1:3]\n",
    "\t\t\n",
    "\n",
    "\n",
    "\tdef _get_pre_M(self):\n",
    "\t\t\"\"\"Build the sel-CNN graph and returns predicted Heat map.\"\"\"\n",
    "\t\tinput_maps = tf.pack(self.feature_maps, axis=-1)\n",
    "\t\tdropout_layer = tf.nn.dropout(input_maps, self.params['dropout_rate'])\n",
    "\n",
    "\t\t# Conv layer with bias \n",
    "\t\tkernel = variable_with_weight_decay(self.scope, 'kernel',\\\n",
    "\t\t\t\t\t\t\tself.params['k_size'], wd = self.params['wd'])\n",
    "\t\tconv = tf.nn.conv2d(dropout_layer, kernel, [1,1,1,1], 'SAME')\n",
    "\t\tbias = variable_on_cpu(self.scope,'biases', [1], tf.constant_initializer(0.1))\n",
    "\t\tpre_M = tf.nn.bias_add(conv, bias)\n",
    "\t\tself.variables += [kernel, bias]\n",
    "\t\t# Subtract mean \n",
    "\t\tpre_M -= tf.reduce_mean(pre_M)\n",
    "\t\t#pre_M# /= tf.reduce_max(pre_M)\n",
    "\t\treturn pre_M\n",
    "\n",
    "\n",
    "\tdef train_op(self, global_step, add_regulizer=False):\n",
    "\t\t\"\"\" Train the network on the fist frame. \n",
    "\n",
    "\t\tArgs:\n",
    "\t\t\tgt_M_sz: tuple, shape identical to self.pre_M,\n",
    "\t\t\t\tGround truth heatmap.\n",
    "\t\t\tadd_regulizer: bool, True for adding L2 regulizer of the \n",
    "\t\t\t\tkernel variables of the conv layer.\n",
    "\n",
    "\t\tReturns:\n",
    "\t\t\ttrain_op:\n",
    "\t\t\ttotal_losses:\n",
    "\t\t\tlr:\n",
    "\t\t\"\"\"\n",
    "\t\tpre_shape = self.pre_M.get_shape().as_list()[1:]\n",
    "\t\tgt_shape = self.gt_M.get_shape().as_list()[1:]\n",
    "\t\tassert gt_shape == pre_shape, 'Shapes are not compatiable! gt_M : {0}, pre_M : {1}'.format(gt_shape, pre_shape)\n",
    "\t\t\n",
    "\t\t#with tf.variable_scope(self.scope) as scope:\n",
    "\t\t# Root mean square loss\n",
    "\t\trms_loss = tf.reduce_mean(tf.squared_difference(self.gt_M, self.pre_M))\n",
    "\t\t# tf.squared_difference(x, y, name=None) try this! \n",
    "\t\t# (x-y)(x-y) \n",
    "\n",
    "\t\ttf.add_to_collection('losses', rms_loss)\n",
    "\n",
    "\t\t# Use vanila SGD with exponentially decayed learning rate\n",
    "\t\t# Decayed_learning_rate = learning_rate *\n",
    "\t\t#                decay_rate ^ (global_step / decay_steps)\n",
    "\t\tlr = tf.train.exponential_decay(\n",
    "\t\t\tself.params['lr_initial'], \n",
    "\t\t\tglobal_step, \n",
    "\t\t\tself.params['lr_decay_steps'], \n",
    "\t\t\tself.params['lr_decay_rate'] , \n",
    "\t\t\tname='lr')\n",
    "\n",
    "\t\t# Vanilia SGD with dexp decay learning rate\n",
    "\t\toptimizer = tf.train.GradientDescentOptimizer(lr)\n",
    "\n",
    "\t\tif add_regulizer:\n",
    "\t\t\t# Add L2 regularzer losses\n",
    "\t\t\ttotal_losses = tf.add_n(tf.get_collection(tf.GraphKeys.LOSSES), 'total_losses')\n",
    "\t\telse:\n",
    "\t\t\ttotal_losses = rms_loss\n",
    "\t\ttrain_op = optimizer.minimize(total_losses, var_list = self.variables, global_step=global_step)\n",
    "\t\tself.loss = total_losses\n",
    "\t\treturn train_op, total_losses, lr, optimizer\n",
    "\n",
    "\tdef sel_feature_maps(self, sess, vgg_conv, feed_dict, num_sel):\n",
    "\t\t\"\"\" \n",
    "\t\tSelects saliency feature maps. \n",
    "\t\tThe change of the Loss function by the permutation\n",
    "\t\tof the feature maps dF, can be computed by a \n",
    "\t\ttwo-order Taylor expansions.\n",
    "\n",
    "\t\tFurther simplication can be done by only compute\n",
    "\t\tthe diagonol part of the Hessian matrix.\n",
    "\n",
    "\t\tS = - partial(L)/patial(F) * F \n",
    "\t\t\t+ 0.5 * sencondOrderPartial(L)/F\n",
    "\n",
    "\t\tArgs:\n",
    "\t\t\tgt_M: tensor, ground truth heat map.\n",
    "\t\t\tsel_maps: tensor, conv layer of vgg.\n",
    "\t\t\tnum_sel: int, number of selected maps.\n",
    "\n",
    "\t\tReturns:\n",
    "\t\t\tsel_maps: np.ndarray, conv layer of vgg.\n",
    "\t\t\tidx: list, indexes of selected maps\n",
    "\t\t\"\"\"\n",
    "\t\t# Compute first derevatives w.r.t each feature maps\n",
    "\t\tgrads = non2zero(tf.gradients(self.loss, self.feature_maps))\n",
    "\n",
    "\t\t# Compute diagnol part of Hessian which are the second derevatives\n",
    "\t\t# of Loss_x w.r.t x\n",
    "\t\tH_diag = [non2zero(tf.gradients(grads[i], self.feature_maps[i]))[0] for i in range(512)]\n",
    "\n",
    "\t\t# Compute the significance vector, with each element stand for \n",
    "\t\t# the score of each feature map\n",
    "\t\tS = [tf.reduce_sum(-tf.mul(grads[i], self.feature_maps[i])) \\\n",
    "\t\t\t+ 0.5 * tf.reduce_sum(tf.mul(H_diag[i], self.feature_maps[i]**2)) for i in range(512)]\n",
    "\t\tS_tensor = tf.pack(S, axis=0) # shape (512,)\n",
    "\n",
    "\t\tvgg_maps, signif_v = sess.run([vgg_conv, S_tensor], feed_dict=feed_dict)\n",
    "\n",
    "\t\t# Retrieve the top-num_sel feature maps and corresponding idx\n",
    "\t\tidxs = sorted(range(len(signif_v)), key=lambda i: signif_v[i])[-num_sel:]\n",
    "\t\tbest_maps = vgg_maps[...,idxs]\n",
    "\t\tprint('Selected maps shape: {0}'.format(best_maps.shape)) # e.g.(1, 28, 28, 384)\n",
    "\t\treturn best_maps, idxs, signif_v\n",
    "\t\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_selCNN(sess, selCNN, feed_dict):\n",
    "\t# Initialize variables\n",
    "\tglobal_step = tf.Variable(0, trainable=False)\n",
    "\tselCNN_vars = selCNN.variables \n",
    "\tinit_vars_op = tf.initialize_variables(selCNN_vars + [global_step], name='init_selCNN')\n",
    "\tsess.run(init_vars_op)\n",
    "\n",
    "\t# Retrive trainning op\n",
    "\ttrain_op, losses, lr, optimizer = selCNN.train_op(global_step)\n",
    "\tprint(sess.run(tf.report_uninitialized_variables()))\n",
    "\t# Train for iter_step_sel times\n",
    "\t# Inspects loss curve and pre_M visually\n",
    "\tloss, pm = [], []\n",
    "\tfor step in range(FLAGS.iter_step_sel):\n",
    "\t\t_, total_loss, lr_, pre_M = sess.run([train_op, losses, lr, selCNN.pre_M], feed_dict=feed_dict)\n",
    "\t\tif step % 20 ==0:\n",
    "\t\t\tprint('Step: %s  , %s Learning rate: %s   Loss: %.2f  '%(step, selCNN.scope, lr_, total_loss))\n",
    "\t\tif step % 200 ==0: pm += [pre_M]\n",
    "\t\tloss += [total_loss]\n",
    "\treturn loss, pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading the first image...\n",
      "Classify it with a pre-trained Vgg16 model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/repos/FCNT_bak/inputproducer.py:85: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  roi = convas[cy-half:cy+half, cx-half:cx+half, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "barbell 0.2071\n",
      "dumbbell 0.183466\n",
      "photocopier 0.0472396\n",
      "projector 0.0431489\n",
      "printer 0.0379688\n",
      "Forwarding the vgg net cost : 5.23 s\n"
     ]
    }
   ],
   "source": [
    "print('Reading the first image...')\n",
    "## Instantiate inputProducer and retrive the first img\n",
    "# with associated ground truth. \n",
    "inputProducer = InputProducer(IMG_PATH, GT_PATH)\n",
    "img, gt, s  = next(inputProducer.gen_img)\n",
    "roi_t0, _, _ = inputProducer.extract_roi(img, gt)\n",
    "\n",
    "# Predicts the first img.\n",
    "print('Classify it with a pre-trained Vgg16 model.')\n",
    "t_start = time.time()\n",
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())\n",
    "vgg = Vgg16(VGG_WEIGHTS_PATH, sess)\n",
    "vgg.print_prob(roi_t0, sess)\n",
    "print('Forwarding the vgg net cost : %.2f s'%(time.time() - t_start))\n",
    "\n",
    "## At t=0. Perform the following:\n",
    "# 1. Train selCNN network for both local and gloabl feature maps\n",
    "# 2. Train G and S networks.\n",
    "\n",
    "\n",
    "## Train selCNN networks with first frame roi\n",
    "# reshape gt_M for compatabilities\n",
    "# Gen anotated mask for target arear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1ee02ed860>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cuHbZs82oI4iyD3Ri0yHW6/Wcs5JgOhqNUC6Xsba25iRhqVRKJPzYtRMamWB/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\noB+BQKFqFztb9ziM4+fhSZoWpVLJJSmNRiM8ffoUx8fHztnI95RKJVQ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DFY+IVZKy3jWm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ee65f1668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(roi_t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/repos/FCNT_bak/inputproducer.py:85: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  roi = convas[cy-half:cy+half, cx-half:cx+half, :]\n"
     ]
    }
   ],
   "source": [
    "roi_t2, _, _= inputProducer.extract_roi(img, gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26086479"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_t2.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1ee01cf470>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cuHbZs82oI4iyD3Ri0yHW6/Wcs5JgOhqNUC6Xsba25iRhqVRKJPzYtRMamWB/\nTCYTZ4JQO6AWQvOh2+1iMBhgZWUFzWYT9+7dw+bmJiqViqszmZdjwYVc6h+p1Wq4deuW8xGUSiUX\nXfExpi6VthqNvY9zRjNBp9Mp1tbW8Nprr2EwGDinJr/nmechkzNECw8IIfL5Anzf7b1pPoQ4jt3E\nZTyd36fTqZMwnIh8jtLUMoyq9fxwkqjNa8HEV4a2SyWnbgWuKw9VBd3Y2MCdO3dw584dtFotFItF\nDAYD9Ho9l1OgPgJOUrvduNUeeG1lZQXdbhflctnZx2zfcDhEr9fDYDBI3B9FEZ49e4bpdIq9vb3E\nbk1cF2GXW7ONlUoF9XodjUbDmQLj8dgxLT334/EYJycn6Pf72NnZQbvdRhzH2N7edjZ9o9FAo9HA\nxsaG65tSqeTCh+xL1o1AQC2CPhE7RqyzT11XQGP52u/0FdBE2d7edj6Z8XiMZ8+eod1uB52Kvms+\nn5uPXglAsAwNpC9esd9n+Y0D3O12HcPQkURVU0NAdrMMvabf9S8HUiUIyQKHLV/rSXuUz3OiadkM\ne9GOpVe73W67dQdcq8D3D4fDRIquvl/bqObAysqKs4tp+nAS0xlHbYWZitQI2u12YmvzUI4/zYC1\ntTVsb28DgHO8kUHVOUlAa7VaLmzKsaS5Va1W0Wq1sLa2hmaz6bQDqvyacwEgUT4BmT4LzThMm2+q\nJdC0Y115Tc1OZoN2u12XPt7tdi9kKqaZBSHfmqVXAhDykA80Qr+FNASNxVOCTqdTrK6uOkePdrra\nzZQayjiWgdUWtr+TsgBBJYk1W3w+DTIr7+l0Ok6VpuSnqko1m2E1Skb2jfaXRlc0WYdMS8aI49iZ\nLpTGTNCh74PvZp3UAUotiqBCUGs0Gk5bqFQqCU2J/RxFkcsBeP311xN9Vi6X0Wq1nPOvXq+jVCol\nGFXL8tntegK0AogCmo69jqmuFF1ZWUmAK80VPl8ul1GpVBwQPH78+MI+FrZuvvllfVM+euUBIYTE\n82gInIiUHqVSCXEcO/XVlqFOQPXy61+fD8RKW9+91u60YKc2vJWkrBvrTUlGD7UFC5s/r+1RXwIZ\n1wIW60ZtRaUo60Jn3ubmJtbW1lzKs9bNLhpifdSnUygU0Gq1cPv2bRei0/GhCaH95mujZlfSycrr\nIbON17VONJ3YTpbDcdA5o74hdaJOp1MHeD5Nkrke1WoVjUbD+TWshhDH4TC0zp1XGhBC9o6P0S5L\nHCg6izhyU440AAAgAElEQVRYKysrCZuaHa8qut0ww1e29VdYk0EnUGgi2Y8yFf0RrBsXFFmppKBB\nJuJkZpvIZJou2+l0nDPLtgc4Bw9lRmpX1WoVa2tr2NraSnj50/pB1W+aH9TYarUaGo2GqzcBnAzD\n+rAObKNqUxqBYUIR11xo0pSSaoZ6epMmYPE+HUt9H30kGt6NoigBsiyf84oaSK1WcyDIeqqWwPJV\n4Fit55UFhKtm+CxSZ5lKbisplKnzmAI+VU21kZDDx75HiRNMVXXg3LFIXwgnMBfMcDJWKhX3XjKM\nqv/03J+cnOD4+NiZGtQAtO72o22mut9ut/H06VPnDOSn1Wqh2WyiXq87RlYnLRN9mOPAv9VqFdVq\nNSFlyVRnZ2fOD0BS/4Tdcl7V98Fg4JhUmd1qGBwD9V8oILAPSKwfn9f5BZzncBDcdZs2OroZEYqi\n59mKHAuds6qZ6Tj45pWPFhoQXjZZaeVTFUmqulv7Pi+QqYTn+3118pWpQMT/1V+gk5cTRicNIwoA\nEvaq2sf9fh8nJyfY29tDp9NJmCkaCrUS3f5GXwyfLZfLqNfrLm//9u3b2N7eRqvVSmSAktEICvoh\nOHBs2FZKVGoSlMSa78F7FQxINFt0RSPrbcdBVX/1H/k0Ph03FTC8Rs1Uw4m8h9Guo6MjHB8fYzqd\nOvBYWVlxW7qpU1LnSJpGYOlDCwjzaBc6oHRm0WTg4FLqcCLSrqWEyAIGK/VVovgAh9996jnrxklL\nKbS6uuokKAC3PoF2qgIEmYEhMEp0jTDYpCGCoE/FZ6KUahHFYvECmEwmzzdc2dvbc8lfXNzTarVQ\nr9fRarVcaFFTosfjsQtjss00daj+M9rBZCEFPQ2hsi8IRGp6sb7sWw0Xsw+sv4LXbD5JyJek84xm\nj5orzFTk+hKC88rKClqtlgNuOoJVcFnfgW+eWfrQAsK8pJ1oEd6q+D7TwF7nRMhCad9kCT3vM1tY\nN5KGJHVBDpnFmkUKEJplqEkzPpVY+8jXBjIacK5ik8Enk4kLfzJ5iGFFTd9V8GViji4iYiiTUQIC\nGyUmf9OsQfYr7ycwMAeAQMZ22DwMNSe1f3wCgeOjY2n9QOxDjuHZ2Rk6nQ6Ojo4cEHC9SaFwvhcF\nIxNq7mm0RcsMzTWlhQWEWVXvqyBODk7carXqzdDTwbRrCHyTgX9Dk8J3v363zki9pmYAtQVKUK7O\nY5KVphKrSs56qGNPw5L8nYxjowgKCJRsajrQSUnveK1Wc4DAxVWdTgdxHOPOnTtu5V+z2UyEgDud\njktyIrNXq1W314ImKNEfoU5Q1nE0GrmxY7s1rZqOVt+GqD4nne0Ha17yd5ujwHpWq1XXTgL4wcEB\ndnZ28OjRIzx58gTPnj0D8NzXsLW15SI19A1pkpudc75xCtHCAgKQnjPgU8EuQ4r+1ofA91GtVgdR\nnk62lKVJhFQ9Xzn60YgAP+qt1nX11s+g2oFdw2C1Jp8WxZyAZrOJtbU1F9uvVCrO7tcsQK72o+Oy\n2+2iUChgNBphd3fXlTWdTl38nanPlIAMrSoI2b4kkPEZ4NxkUHOAZhKv645HCpJajuZaEDztik8L\n8Bbg2B71E3B9yfHxMbrdLk5PT52WU6vVnAZVq9XcOgw6grX9ahLa8QrRjR8HD/iXudp70r7z/zxg\n4bOnVAJYtZlqr0VYi74++z9kBth32++WfBoEmVpz8Hu9Ho6Pj3F8fIzBYODqzslN/0AcxwkJqMk/\nzDa0jkgACT+C9gc1qvX1dXzkIx/BJz7xCXzsYx/DnTt3nCTTtGgFVO7M9IMf/AA7Ozvodrt47733\n0G63sba2hlKp5JhFE3eYCMa+4G/so9PT08SO0WynL4fCp9n5Iksa4rNjznsqlUrCr6OgSuDZ29vD\nzs4O3nvvPezv77vsQ45fr9dz6yu4enJrawvVatUdfsPl1dSAGBVSE5DjCsD5uHRlrI8WQkPQgcyS\nkFmg4Ptuy1P09g1qVl1D9QrVfxbTJ8+9ansy7EXbut1uo9fruew5BTO1i6kqs81U61V7sI4xTmr+\nZa7DxsYG7t27h49+9KP4+Mc/jjfffNOBAeunKrpOWt3DsVgs4vHjxzg8PMSjR4+wv7/v8hUAuKgB\n111QGqsjUz33CvBsp4KB+gJUKLBvKHW17UAyykKHJLUVPktSAUNA6Pf7zhnNrM2trS03LuprqVQq\nrv2q9RH01N+hmkmhUEiEZguFQsIvFKKFAASSVcMXjWap17wmTdZz2keaTUkfgS6l5SRRia7SQRlC\nk2U0BdeG1fhuTfq5desWfuzHfgw/+ZM/iR//8R/H3bt3Ua/XnVagy5Ft/F/NFt57fHyMo6MjRFHk\nVGMm5BAMFBB86w6s9mCdhz4NgQBC8+v09PSCdsg+JAgQhFgH9quWredZ6F6JTKvmhiv1ej2xlJpa\nEMO/z549w/7+fmJlJ4GZANLv9wGc5zXohq7dbhfdbtdt/+ajhQKEq6Y0Fdz+H1IdgYtx3auiWUDD\nMiSlQqlUAgCXZUfbXcFAQUKZgm3iX7U51dnFlGd6txl6LRaLWF9fx+3bt/HgwQM8ePAAW1tbaDQa\nCXu+Xq87iWZtWrWpR6MRVldXsb29jXK57BiHiVS6cxPBRaW3akLK4KrVWFNITQI1Byj5NZ+CfW4T\nlFRas1/VmapmXb1ex9raGu7evesSqBToNGTLHASuGiUA0GzSegwGA1SrVQe+XDuyubnpwrg0C4fD\nIb71rW9559mHGhBmISsprpr5sygPOPi82goSTN0FzhNpuGsRVUVNh7VSVFVbSnRV06ktUCvhGoW1\ntTXcunXLTb5KpeJNglIg4nsJCPSwE8w2NjacekwpSADQiAqABJBZjUOXpVtzQQFBI0zMk9D3sB2a\nSKakOQo0wSiZtd4aeaHJpVmjauZo1iSTjzQ6pE5RC14Kjtx3Qp2NIcoEhCiK/gDAPwCwG8fxT724\ntgHgXwB4A8B7AL4Qx/HJi9/eBvAlAGMAvxrH8TdCZeukvErKcuaFmE9BIM1XECr3smT9EL7yFQj0\nODE6QDm56LlWRxUjCCxHvenUBGjfstwoihIpwmy37jKkZanEVobTyakquZ5DwGQblkNHGfcTZD1p\nK5M5len1oBlrCviAwKarK7Pqhikay1dmIwOqs45SXHMWrDnDMieTidvI1vpofEfn6bu1H/WIOQ03\n07w4OztzKeKVSiU4B/NoCF8H8E8B/JFc+zKAb8Zx/DtRFP0GgLcBfDmKor8H4AsAPgngAYBvRlH0\niXgOzpnXBr8M6UTOQ/OCWcjZmAU29ncNp5EZFBA4oQuFAhqNhrNhdWWhqrrqeFPVVNN/dcJWKhXc\nuXMHDx48cLsIWw82GcNOYpV6+ruNnDCURsZVDYX9ofUiw1jpr2CgjKcJWbxPnXQsk9dVe+D7Cap2\nPpCpyfwKGBYwfXXUFGqdm3qPXSlKsFEw4TjQf0Et0keZgBDH8b+OougNc/kXAfz8i+9/COAv8Bwk\n/iGAP4njeAzgvSiK3gHwGQB/FSj7wrWrtNezfAjWZ6CIPo/24os0+O4JXc8TlbC5A1QhGWJj2At4\nHmbb3Nx0bWJmIDPfGOemZNRNUu02YQASkrxareLWrVt4/fXX8bGPfQyvv/461tfXE6ndPvV1Mnm+\n5wKTpWh62Em8srKC4XDo8g+4PwPrqKCtzEIA5HWfL8GOv2VG9qdKZR0jm63KvtXcDn1WHbUKYqwr\nHZM06VTj0eXgfA8ZGzg/DIYaIhPquMZBz4/QnaNDNK8P4XYcx7svXvQ0iqLbL67fB/CXct/Oi2uv\nBCk4zEs+MLjq6ATtWarOBANmAiowqWpNx2O5XEaz2cTx8bE7r5EZgKyDHlFGdZ2MWqlUsLW15cDg\n4cOHWFtbc5mCWn9tu1WDyRAM31HFJUDRv6C7N3NiU4OwTj6G8yzDpvWpmmGaqk3JT2ZXqU6mYtkK\nfmoesHwbxmVdFZTU16NAo8KJ9aGDkeFmNb3oC6HTmc7bTqfj2hKiq3Iqzs1BdvLo/1dlMsziQ8hy\nKFq1XQc9dO9VtMNKWo19M6+d0pmTSHcApk+BKw23t7fR6/VwcHCAJ0+eYGdnJ7GBijKD7vHH5Jit\nrS13AMmdO3fcOgINhVlm5URWU4G/cefkw8NDF+5TZmS7ATgzgmaLbiKruQQhbdNKfN+YWrXe97HO\nOQKD3ShFw5Sa3EWNQFOOfRvEqDmimgPNLvXFsJ2FwvkuXjR5GKpkZMpH8wLCbhRFd+I43o2i6C6A\nvRfXdwA8lPsevLjmpa985Svu+1tvvYXPfe5zc1bHT7OaDL6JkyVZ0oAjCwiyzAcfkOkEppeaJynr\nNm5A8jh1qwarA3B1dRUbGxvu+Lbd3V0cHx9jMpmg2Wzi7t27WF9fd8zHjVu3t7cTHnLNBbBgyNDZ\n8fExdnd3nclC6U+V3QKFOuDUA2/7kPa+1Q74146tb6zVzlfV3/fRMtUPwf5Ux7D2h0p/jWwo2dRx\nag0cb9VYmDjV7/dRLBYTPhm2I45jHB0dudyOnZ0gS+YGhOjFh/TnAL4I4KsAfgnAn8n1P46i6Pfw\n3FT4OAB/wBPA22+/nfg/zadwXeSzKUMq78uoVwhILBiQUXSzEE5EnWBkIjUD1A5eWVlBvV53pws1\nm00A54ubtra28IlPfAIPHz50i4gAuB2oNTWYoMM26KTmgp0PPvgA3/ve99wRZdwevV6vJ9Y+qIpN\nUiYCzs+l4KTXhVo+zS1N+1Tty9d3ChisB30i+i4LKCyHUt2CGZ/VkCHNCd2ZScdQcxBoWtldqm0b\n19fXXT7IxsYG/vZv//bCHAPyhR3/OYBfAHAriqL3AfwWgP8ewP8eRdGXAPwQzyMLiOP4u1EU/SmA\n7wI4A/DLcYaYvA4T4UW9g+9KY+arrMMsZFXXrHs1Q4/qNZ1XZAyqo1Sx7b4ONuFGFyjdv38fp6en\naDQauH//Pm7duuW2ZLMblnASqyONEQxO5t3dXXznO9/Bd77zHXz3u9/F06dPXdLN6uqqO+1It0en\nT0STquI4TqjgABKquzUH0jRAn41vzQUNV/Je3VtBtTG7J4beoxoEy1EAtxmXBAM+ZyMOamLwPo3W\nkLRvON7MZvRRnijDPw789PcD938FwFd8v71MSrMb9ZpOZl/4KsvsyKJZ/AhWU/GVRWbkBqHUDFRV\n1vKsBqTaASclcG6PczHNnTt3nORh+jCZUD/KHHyHTmIC0tHRkVvQQ+1AVWwuP7absmgikE54q7Ir\nY7KvtE8UcH2+AO0bvUe/W6eh+gN0HYPWQUFJy1Qg0HdqXoSaInZTGzoQeW4I/RB2nvu0JS4d99GN\nZyraCuexvfOStZdt2WQw2/E6OUIaTJpZwff4HFZaB58k8YGBTnaGlbg3oa4ZIFEt5eRkmE5DXfyu\nvgS2uV6vBzUW6y3X7+xz7cuzszN3zNp4PHaLoRi9UInPJB2elcAwo5XItKvJSJTM7HsFDpvBpyBg\nF/qo3U3SRCa2jyq9vtf2J4DEfgosX/uemk+hkNwGX1OMVctSRyKjQkzc8h0WQ03S+j3SNNEbBwTA\nz1DA5dT3kJQNmQ2qHfjqFaqL1Th4r6/TQ1qK73frWNM2KfPq8mZ9RtuvKb5q5yppoo4m0rAu1lbW\neqvGoaaD2sSssy66YZ20zTRzdFcnagwsm2XpkmfrzPRlFtqx8AkJa/tr36gWaROXNC+C4VmCpTWt\nFBB0CzXdj0L3sSAI8MOMUg0X67ZvbJ8VSnlM0oUAhOugkITj5NRwWFriiv5luVoWr80LXj71kv/b\n+pBheC6i2pS2btpWflcmsJJeN0YhaKh0phal6royq1XR1S5WXwcX/NA3ACRXBypjMESm4UTtG60P\n72Fb1bmpmhGf14gCgVLHg98VKFXz0b7RcSOg8j08/4HLmNmXdsx0DvKvagQ0qXiq1nA4dEvd1b/g\ny8FgW9TECtFCA4JPxQ8xoc9WSrOplcl8IaW0+oQ0msuQr11WTVZJbQHB2qtWQ/LZs1aVpU1MQFDH\nnQ/0FFhYpq0D1WDd05H2rw+Mdden0WjkYuYqha25om1VtRw430hGNR6WoZqSb86o5mPNBi7d5u+0\n69WpS38P22j3bNA2UFPQD7UALltmxiaBgWsULIW0Ux2bEC00IABhcyLrt7xknT3z1uuqyZpNKukY\nf9aY/SxaigUK1RZ0Muv+BcoUPi2K3+2H+Qd0fnE/RDIs26XSnc+MRiOXNm0ZVgFc62FNCdab7VAQ\nSMvYU23KmkM2FBlFkTs0VyMF1IYIbAoW/LCP9VBc9lWn08HJyYnLJtUj9tTJqQDuE4J52ktaeECY\nl7KYw06wkGTNej7v+9LISjhlNv1QMjF7kHa2aggKEFov3qMAaCeLJvdoDoBVZ+07reSx2kG323WT\nmvsiUnJrlIP/q4mgDMh6qrNX/RPKsNY8UE3C9o36OrRvyNT0GQC4oJVZPwN3dNa6sx5MJebeDmw7\n04rZRzxomCsVCY4k9U+oH0lNGJKdT+yPEH1oASGNrKNlFmnv01AuAwYkO8FD5ZIhmbKqkiKr3mn1\n9DmflLE19EXGCZWvAKbecT3+TO1dTuRSqeRCqdxLQMHGtsEH3vpeABfAwNe32ne2TJtfoBqSdSRS\nM1Ew5IerHtlGJmCdnZ0lTsjS5cq6S3Ycnx+aa7U6+7Hts/2zsIAwjyTOW66VBra8LC/0yyCfFLd/\nrYSjfWyTVUI2vvVJ+NRgy8D2WdUMOEHVOcZ6qQqtfgluEaYHq9KhSKbixqFra2vu+Hpdn6F94Rsv\nvUaG1D5WB6o1E7U/dHEQn2M9VXtQx6D6Puz9ugu2bp5CsOP2aDSnWKae5cg6poFASDiptqXzIEQf\nWg1hFpPhpgBB66IMqEyszi9OJjKnmgxZ5o4ygU+6A8nDWpX59B5lNqrTaVoNdw7e2trCa6+95g4a\nYbYcy+LuSzwIlqE6vkNj9soEaeOmwKQfq+WoGq39aM0O7SfNXiT5yrDE/ufY6cYmuusTzSdduGW1\nUzt3remp80nNmzTe+FACgq/BPumpf302vE/q5il7FlJ7WMvTyWq948D51umatspnrSRQMNDdd2ye\ngPoKVJ3XGL/a8z4wtROyWCyi1Wrhtddew3A4xMrKCjY2NtDv9xMAFUWRS5vm+gi2Ve1nAqQFK1sX\nn32teRUkyyTWHPCZKZrUxLoTwKzWRsdiHMducZmaYWR8ahEEGd1E1joMbR0t+Pg0KY0EvbJhx3kp\nZC9bM0IRP6RSpZV1FZQGVCFVX1VUXUDEiWalptUOrFOQz/lCsNQYqJEwuqFbhlnpZdtTLBbRbDbx\n4MED1Ot1PHjwwDEDpaUeocZrrAO1IltnJQ0r0u5XZlKm8jGUL2NR+83mISjjA3DtsesN2H7VGtTc\nYN3pH9BdrlgHq4FZc2hWeqUBYV7G8zGFT7W1gxQCgKuqV1pZnMx2sli1UGP2mvOugKCONMvkqlFo\nmRoKpP2rufO68y83WgGSS6kVWPmXqxlrtRru3LnjMvmYVssTixiFYNxdj3BPGxfNmWDbyVwKCqr1\nUIIDuCAMFCTZJgsE6hy12pXWie+z888yvI61ttOad9ZkY7vsXCJZ4ea7R2nhAWFe8qGnDxCss8in\nAlvnmz6f9c40slLKen+tb0AnpoYgtf5U931goJ53CyjWrGAyUbfbRbvddokx4/EYpVIJGxsbAODO\nS9D4OvtCVVdlDp7czLYw9ZZZfpVKBdPpNBFzt0487TPVCKgpWO1A66LPKfMDcO3Q/lQNwYJryG6P\n4/MdkRgx0DFVIGfbSWqiqdmhmZU2NGr9BD7/is6LEN14lOE6nXk+qWIHTQfamhQhjeE6tAPL/BYI\nAL+00MlqVV8+r2E/tpNhS05aMgdDmp1OB4eHh25jjU6n4/bnq9frmE6naLVaiVOY1TNvHVoaEisU\nComcfd1ZmCo2t3LX3ZG0r6yz0AcCqhVoPeI4uaDK55SzYJA2Nvq/dUDaJc/aD/Q56LgoKGvKtbaH\n9WaWovUZWKZXkPb5rJRuFBBUbbM2Mq/5JHIIRCxSW1IHC9HUIr1l0FAZtk7zkpUGZGqNBrCfCoXn\nKxd5DyfG6emps1M5iSjpJpPnJxufnJyg3W47jzYThAA4Z97KygpOT09xeHiIJ0+e4OnTpzg+Pk6c\nvMSkGjUrmIrMxBtOXHVQxnGcWLREUlXbLvPl+waDQQJg9D32BCdfFMKG5dTPoPOFoKTOV5XSmhCk\n5SkgM4dCT3JiGXbJuAIZgZsJSoeHh2i3225FqG6Sou1QU03npZohNj/Bt7iNtHAagjJZSEL7KGQf\nhYBBEV41BJbhAwRbl7x1y1v3rHdzEpdKJcdc+/v7GI1GaDQaiROWKVlOT0/R6XTw9OlTPHr0CAcH\nB26VHBfptFotbG5uolKpJABhb2/P7crMY9xXVlYSK+76/b7bm8Haytb3ocvLQx+VjDoevKb+gtD5\njpoT4WNgq2GwzDSprwxGICLgakSBx7NxLLSuPPBGwYW/0ZHK05+5xJ1JSprgpVoH66samtXO7CeN\nbhQQNOEGuMgM1qYOmQD6l2S1BfssJ5k6idK8r1n1mJdCOQE+zUlt8en0+THpe3t7KBaLWFtbw+bm\nJjY3N1Gr1ZzjbjAYoNPp4NGjR/j2t7+NH/7whzg8PHRSl6f6bG1tuYNV+QxBQ98/nU7d3ojck4FJ\nR2Qq314FCgQ+O1zbyn6hH0Nj9Oo49AGDBQQLSmmAa9dtWBMMON+8VAGkUHh+xB0dpwQEaiwEAo0k\nqCNWfQkEiWq1iu3t7cRqx3a77VY4aogSOF+jYSMpJO3jNLpxQADC0t2aFD7zwadFhBwrPpNEJ5aG\nkV4GhWy9LGI7JpMJer1eoo/0UFFO7NFohHa77dR3rsADzhlWDwCl9qGSdDKZuI1OoijCycmJe4ab\ntnAhkpo0DCP6zDFf9EMPHlEthGYR28kxs0usbRKS9rVVq9P61jc+7C/NCWD/cOfrLEBQPwdwngxG\nX06lUkEcP1/NSiHFBU+alKYmo7ZL/QS++bXQPoQQEJC0QWmmhS3Tahg6yPo7J5Umlag3O00TuEot\nwVe2kqqubJcyG6Vpu93G0dGRs//H4zFqtRpu3boFAO58Pz05WJkLOE8pVokJPAcJrsYbj5/vg3hy\ncoIoitBsNhMbtJL5uCmIjRKo3esDA+bzc3Uf32nbr2OnDGgjCAoGPp+R9q2tlzUV2C/WT6GnUts9\nIOwBtQpWqhExzRuA+xvHsUtv5g5JdORyvDSCYQHBp4EuLCD4YtZWRQ7Z7FbiKMPbZaDWLiTpnn+a\nBqzv0r+2Dpcl1tW2UVVWHVhlJLVdATgJozvw0Evf7XYBPM/4KxQKqFarTiPyhRxtfgOJW4BTje10\nOjg4OMCzZ89wcHDgpCO1DtZBy1QTTRc76fv4OxmAE75cLnszEC2j+QAhj59GzUgAibKtg9BGTUql\nkjs5S8GBgOA7+JXvtHNKHbLMw2A4ttVqOb8PtYdOp3MhUsOy7VwDLpqpSjcKCDZjK+QEsczoswWV\ncXUjirRIgoa96Cn3kZXYdnLNSyH/h72m3m4bF2c7dccjRgTYZi6aIWPRDmf9Ofl8TMnJo1I5iiKX\nP9But3F4eIjDw0O0Wi3nl1hZWUmAgEZO+A67+lGJpk6v18NkMnGLnCzTW+9/CBB80tJHrGsURRek\nvR0z/U5A4LJmfc4HCNr/CjJkXOvXYr82m03UajUH9u122/ER/TQqPPisda6G6MYTk2zFLSPY6z4/\ngqqSPo+ylmE7hM43m72Wl9FnvT/reQU2n4RTjYDxf7sTT7lcdoe78llNFeZk0iXUVDNVA7COK5om\nGmKMogjD4RCHh4eo1WqYTqdoNpvOC699r+Dj09jIDGQgthNIrk2w39P6NuQP0H7VOUHmYSShXC47\n+z/N32OzNUOmib5XNQwFaZLV3mhS8DlurEozgv3L+tjQa1pfkRYCEPjXMoFKJ99zOqCh59UGtPfY\nuO5VtGNW8gFc1nv0Y3dc5mTmJLbJNZTO9FzTeah5C6o1kSH12Tg+34J8MBhgf3/f5TAMBgPnT6BN\nb73qynQcW9UcbHqwNQ18CUeh/tf5paDp0/rU3CTgWUDIApKs8QrVUfuBGoJPE7bmC+tmHaiqcVgw\nSJurNx5l0MQcIOlksde1IeottV5ltVlJ1jus6b+qHs/L2POQgp6vraEJVyqV3Dp5qpUMW9lUX37G\n47GLaTN06HPWqVRTX4V+17pTO3j06BG2t7fx+uuv480338T9+/exsbHhMg7JZJzQnKyc+NxZiSdS\nc9cgmgtW9bZOPdbJJ4ktKHDu6Lzh/74sR5XSofGxIVALWKrV6HVNgrJahW2bmoZ81he2teDm450Q\n3SggDIfDC95W4KKpYAdfzQwAFyaAqmB2oIGkKqZSL4vse6+CrIaQB5AKhYLbPET37idjERzUs85Q\nIh1myhA+k4zfOWF99WN4k4zM9GaCzd27d7G2tnbBA0/tghoFnZP7+/uuHGovBBKG7vi8T+KGJLFt\nk+1vZRCdjwQKCgr2p+/9PuYNmQ3ajxY0fOOvws4Ctp4gzXf7ADGvJnyjgNBut72DyIb4HEYAEoMD\nJDWElZUVd5qRD2ltmqyG3hRt+Z6rBgAfWT9BFvFsgzh+Hq9m8k4cxy7NWHMJeJ3tpX1MbzbfbePZ\n7HuWoecHUuuI4/iC1tHpdFwK7p07d9BqtZz3nSouALeV+P7+vsuMpIbA0CYPotHQYshk8KnnIUZh\nG32had5v+0Tvs2o+n9H1B0DSGatl8B7tZ/pyFKg1vMrEJTWnmLSl80eFnW1b1hy7UUB48uRJQgPw\nIaRe18np0wr40YlLCcPfFLXVA241D363oJAXafOStilNA1KtSb9TG6KtzSQh2v66iMmqvb4+YT14\nhLyaHBqG1AmnDrHBYIBnz54l+nc4HLosSI6lrrGgdnB0dOSAi9oc26cZfjpPQkCgZH1KPlBQAQQg\nAb7g4FkAACAASURBVJRW4Kj5yb7zAZG+R8kCiiWNwNAk9NVFx9POEwI/fR++w2B9dKOAsLe35xxU\n1rakFAf8++PrAPsmhNpRmlfOTrEZYzYf4GWQ1tc3qdhe1kt9HPqd7aBXnKf8ArjAyGRu65yyS2oZ\n5uMzcRxfOE+BE61cLjtQ6na7CY2FdWg0GgnJxUQnbjNuMx81bKfZiGlOxZC2qYDg8wXoHGLkREN+\nZEDrI2DbaNPbOlnyCT1lYo4r+4eRIK2L5hKENFprClMosg4Lm4dw//79hBPHqjw+u9UOsE+y8qMD\nqUymg2LNhxDZAb5qTcG2kdc0Z97+phOMjK6SjGYFzwZgOJH5CCxP38Nr/DAhhu+hj4L15QRj/5Fp\nJ5MJ9vf30e/38cMf/vDCSUrq9OXYces0zgc6T+v1ukumApDQhtRRZ/tTx1nbqYBgNU3VJpWhVEO1\n807NKMv0mtOh/1NzYkZmo9FwoWJqTnQC61JxmhW66pXp6zaxDkBCsLJf0+bsjQLC9vZ2QlJzYHSi\nAMkYslWRfKBBm8oOjgUP65kPdVSW3XUZShscn6Zg2+DTeAiC9MjzlKezszO3I5F6tvkO219kEGpY\nvEf3E1SNjvXgGHY6HRwdHV0wLfjeQqHgVvXRV6AgbgGBx7qRrG8pZELyN+3HkHqvmYShLFI7bgQK\nvUcBgfkiLJ85Hp1OB8fHxzg+PsbW1parG52tzAOx2Z4ERvXxhKIg6tewJoaPbnz5s1WZrN2VJZnV\n3lappU6b62ToqyYLENo+e1+WGqyTnL4FSnl9XjMJVZKyPEqkWq2WOHlY9z0kWTuVkl93ELYSWbda\np7miZxfwcFguGlL1ncCubdd3++xmC6q2T9UU0/9942FNVAtO2s9kZtW8uL8EkNzrkNpSHMcOGLiP\nBe8dDofO3OIuVNbHwrqob2Fhw46qEVhQAMJhJJK1//Q6cPFU4leRfI4pazIB5/n0tVotcY1hKUo+\n5iPYnAKrbWnfkQHJrFZiWXBW9Zp2uQ0Z8r36fTKZOABoNBpYW1tDq9VKLLHWFGbVNoBz+1q1A538\nahJZjYhlWTNB22XL4W/2ndqn1glLsNWEIqY8c+s4toMrH2mCdbvdRM4Jt7Wjb8cHbqyjtoHl++jG\nMxV9Koyv4iHJqYNG0smYBig+ZltE8tVTpTnVW+5vSDWbE0aPCmfIkiYDd1AGLoZzFVgICtPpNOGg\nVduVY6JRIX3e5/fQxCfeW61Wsb6+ju3tbWxubqLVarm8C1X7FYxCaxp8wkK1IPVBxPG549Q+byW+\nBU/dsUnHzGZ36r2rq6u4deuWm6+MvPBMS5ZRLBYTv2lyErUsjQhZrdH6WRbWhwD4K2fVtBCFzAkf\nSNh7XgUgyCLrU6AEVlW0VCq5hCECAyMMqq7TiaZMxr+au6GOLaq/1gFnmclORls+NY9arYbNzU3c\nvn0bt2/fxq1bt1wOA+1m1WysFuPLMlSyUQALHKpO+wAhZKf7fC9WW6Jfi2q9BRTrwyK4sEw9AYr/\nM8ITCg+HTO803loYQLB+AL2Wh3wNDgGCvtMi/sukLH9J3nqplORKQx6bRmbVEBaZ2QKCTmJlADVP\nCAo0BajO2tCYTRVnPcnQOkYEg42NDbz22mu4e/euS2jSPRUoGVlf/lUgCIGBagp6D8tRaasJQnYs\ntN7WTGC77HUFVAsEqkkw0YjRGn6ohfX7ffR6PafVqRahfgo1VXQsQ/NM6cbXMvg6Wv8Cfo0h7X+r\nJobA5ibBwEdZmlGonsxMJNOoN5/MrzkYOnlUZddr1q8DJJcGE1DU9uZv6nPwaWSqsjMbcWNjA9vb\n27h//z7u3LmD9fV1J1nVR0BG01C1aiIk33v1OVs3TTCyEt/G/BVMNJfFvs8HHsqwNn+B5fGdmpbM\nfmq1Wg5caN5Ys0v7QnlBTZcQLcSOScDFgUtjjCwNII+5sSggcBmykpyprXY7MV0HwBwEy/zW/rTj\noeCpk1dBwZoMPgnLd6hmsL6+jjt37uC1117DvXv3sLW1hWq16pKXmF+h77VlK2OF+ooAZFOytf2q\nNSj5tDnVhHiPBVGfCaW+BWv2EDTUaasmlfp+ut2ud9wsOGr9FzrKQNuJpB1DmtUOUpvaetH1nkXU\nEIBsoOLvakeryqoxZ0rQUqmEer3uJiHVUs2JB5DIx1DGo8oaRVFi0xSVzPZQUpW+PknLSc7j3xlm\npIocRecbrWgOBknDpTrmKsVJrKNNgrNgwHrxNzs/2A6fmcIyNXyrpI49rc90Ok2cXammGfuPYVea\nhDSjGPpVxzCf8WnFWaAJLIAPwafq+yZT6JksCjH8ogHBLOSTVpR4wDmzqM3MZciUupZJSVT3+ZxO\ndJIuplFJac0E6y+wGoSmABPEmJnHxKRqtZpoN0HNrs2w0QWth2ozVrtg/9m55/ud331gwHf6hI01\nNVToqb3PvuUGs9SMNKKgORlM1tKVvdYnF2pniG48D4EUGhRFOpUAaeVxElrJohPTpwIvKkj46mUH\nXkNoyqz8jYkuzFZkcpHeT/vSquD6DvX2W/NAx0+lpKrmmt3I5Br6JBSkms0mWq1WYrm0CghVpcmk\nCjLWcajMyzrZ7E91gvrMJiAJCNYMsFvFafnWlLDzjyDOHZY12ahWq6HZbDoNiinNahqkAZHPbAnR\nwgACSZE47fe0e9LeYztjUUEACNdNJYyqvvZZvUZQ0K3CdWWhSix9r3rHdQIqANkNQUKkjjuf7U7z\nhj4QRkCs8zCO4wvnMKhfQO/1OSHJvD6/QUjdZ1/7AEHbpuOjwOirm46VFV58nmse6FsoFotu81ku\nM+cWavQP8f0WlLPMBSAHIERR9AcA/gGA3TiOf+rFtd8C8F8A2Htx22/GcfyvXvz2NoAvARgD+NU4\njr8RKttmV6nam6Neif9DdpOVLLxmn1k0yqobJbUvnKXPq4QsFouJA0UogSht9Tmbd+AL94WiFyRl\nAgtYJN24pVAoJA5/6XQ66Ha77l26kSltaX7IAHqGJduu9VQw8DGwvc6/eo+CizU/LNOlqfDWsTuZ\nTBJ7UVq/A8ej1+vh+PgYz549w7Nnz9xKUeYm+OaGBZrgvAr+ck5fB/BPAfyRuf67cRz/rl6IouiT\nAL4A4JMAHgD4ZhRFn4gDM9uqZBwMVTtflHshGUWXpqo0VCmiG3SybE4OnaAcWLuNtdbRMlro91lI\ny5zlN17XiZMFIPyd/oRGo+ESlXRff98k0glGEGef6SlG3IvRhgkVdGyUgGPJI+cODg7cWDSbTZeu\nrNmJo9HIARxBgqaFL0RI0nfzeihtXu+xqrcCAn9X5tW+tM9yLtMPwmiCamC6kIxt0nLVZxMKxYY0\nFtXyfJQJCHEc/+soit7w/OTjgF8E8CdxHI8BvBdF0TsAPgPgr3xl2xRRDo42CoBjYCZsFAoFx+yc\nLGpTkUE0CUc37GRHUuqx01gnDaUlGhwlN8RQANPJ4zNl0swbC0AKaj4VVieH9X+E7FUth2sFKEn1\n2HU6sLRtZDg6vPS0Z05M9REoENvJqeq21m08HjtnJ5dp3759OwE+wPkekiyP2XpMxFIJy77S+Lsu\nmQaQSOtW5rUmhgoqm3vAOavjoBmD1oYHkOhLvtP6QFimnudIocY206+gSWGsk5orVwYIKfRPoij6\nTwH8WwD/dRzHJwDuA/hLuWfnxTX/yz2NZ+eyUWwkJ5bdcpyAACQRnHYmO5kDpdqCPXuAdVInlo+s\njWrtbpJPk/CVac0dLc9Xtv0/pL1oPe09xWIR9XodwPNNW7lPgmoLwMVkJNWmuCGKMjjVd9q+HCM6\nNW0UQiWlMs7q6qpb7UiQoU9BJzs3EhkMBm68rYrM8Gaz2USj0XD7O3Bu6NxhSM/6BnS8CYLsJ002\nYrn0/mviEBONmGfA+aamiWo02h865ykIdMEY+1PnA8vW+urvPpoXEL4G4L+N4ziOoui/A/A/APjP\nZy3EboihGgKQBASrIZA4YXTAlOk5OK7BMpC8x8be9XdLVvpYCchnlbJU+dD1kHmS9t3W0wIC+4i7\nHHFPRn4UGJjuzN2PrHTXfRupvmtUQNNwreORajPrzzEkQOi5ETpmZAz2t6b8KpiqicnwpZqJnBOq\n6qsGQcZVhlZfjJXEvvwN1o+aAAWQtedD2qMPEHTO2vUK+oxtk+6AlUZzAUIcx8/k338G4F+++L4D\n4KH89uDFNS997Wtfcx3yuc99Dp/97GfdxAMuAgKXx0ZR5BpYKpXcJFIJxElKm5mTl9oCJZzacC/a\n5jrbOsik/e5vmilg+iz1fzsxQijue6fP5PC9ixOE6iYZk+vs6anmxB0MBm5LdIKCal58XhOaCBqM\nEnBcOHmt951jSecgV2o2m013SpHG2y1jaBv50fnB36ixMLuPSU/sd117YX0zwMW0aQAJ7Yb3KyDw\nvRagQmOn/7McbhbM+Uig7nQ6ODw8xPHxcWK7empVCiQrKys4ODjA06dPr8xkiCA+gyiK7sZx/PTF\nv/8RgL9+8f3PAfxxFEW/h+emwscBfCtU6Ntvv53w3HLSaKwbwIWBU4bQ/62GwN9LpZIbFEovRWfV\nKBR5Q1IW8C/KCn3XZ7Su3o7OABitk9YtC3Bsfcm4amPqyjkupqGN2ul03D4KfJ6Tlf1K04Bjyb7l\noiplKlXFGTFgvJ3biXHZs4KBtle/s87KUMC5BCegMQ+DmgCBiytEyVDsW/UXhMwvrYs6I61gsXkQ\nOs/sfAmBi56IzR22uRqUYWU1ewjS29vb+Imf+AlnQn3nO9/xzq88Ycd/DuAXANyKouh9AL8F4N+L\nouhTAKYA3gPwX75o5HejKPpTAN8FcAbgl+OU2a1qlqrebIzaRMqs9FwD515i9cSG7HDeowNl1X0O\nFnAONhYMACQ88ZZJtQzpx6D/wHfNZ7JoGcocel3basuJosj5BOwiKACOmalFDQaDRBSBy29ZNm1z\nSneWZQ+9oQPNhv/YjlKp5BKRNjY2HBDotmnWprdS1ppv2n7+T5+GFSjcmHZtbe3Ctn52nHRuqbZA\n8FDhomeGqs2vfaKObW0PgAsRBm6txj0uisXnh76urKw4raperzszWs0yqz2HKE+U4R97Ln895f6v\nAPhKVrnAeR4CK6iprjpRfSu69K96c4Gkk00niAICiR3F79pZdssvTgLeq2qvlRbKiKG/PsnAd/iY\n2TI9r/ukFvvGtsmXRKSTWZ277Bs6IFdWVhJOWLW5aStzspIBNMxrl0SzrZVKBc1m0+2QVK/XXcRA\nJ7EPwFUiWxDlddVIdL7Q3FGth3s72kNl9P12bLU/6dMg6Gp4XLVX1QDsnLagrvVmmYVCwTmFOT71\neh2VSsWNk4bR1a8QygkBFuA4+BAgqBSxqK8SQn+zHmYfoKgU105SKaQecwAXmMDGu31SKwQIlkFV\n1eQ9VrpbzcMuoLGAoNdsOSr5VFPSulFyMdkljmM0m03cu3fP7RfAdwDPJRdPgSZDxHHsJCJPhVZA\nYDupITAhiZPabsiqTKHvV0Cw/W4dfGwn/UZ6biU3IOn3+05rmE6nCYem3Z1b/SXsC3sKt96nQkSF\ni/KCjgcBSz/8jdoBAZtmF9tCh6xGb9j/ViNWuvHVjtaO56RVJlDVjc8o0gIXN6dQu04/LIuhGJ+z\nS6UQ8+2JzIx3K3j4pDNwMbnIp6qpBGE7rBRShvd5lxXQLKjZemk5of4mw1La1Go155y1phwnWKvV\nwubmZmLbNjK8nq9ggY9l+E5m4tj7zCAf8R5fn/m0JFXJ+b3T6WA6naJer2M6nbpVhiybgGclL+cV\n/SVxHLt2cV5xcxPtOzUxNCKh81DzEOgU9q0u1f4hAKsWqPM7RDcOCIA/NGYnqP3dXrfPABcXfOgz\nFhB8EhaAk5RM3mG9NSZvNQ+rBVhJrxPcvl+v8bqVJFZl9r1bwc8ykTKIVWWtVKtWq4mJRw3Fahzs\nB67U44eaAndvsuq9ApO207bHN44sRyWqBUTV4FQoUNNR7XI6nbp0bm0fmZplaWSBfcX+GwwGLiGO\nDlmOKQ+wBZ6DErMwaXIxC1MPxKHGRiAhIITaaMeV/qAsMCXduMmgqqr+H0VRotNt1pcyJ1Ur1Ryo\n4vFejSlbm1IZzPf/cDh06E71jO8G/IlFlnyAM51OXazeAoJlFCvt1NyxgGOBUSlNiqparuPCvzoe\nytBkAJZHCVar1S5MUmv+qDZnQcLeb+us/am/UyvhHLJnUOjYqaTmmNDhNxqNHEMxs1Xnke5ZQKDk\nQTZ6PwAHCNxGvVgsYjQaJdZhEBBsspyGxjU6ZDNtrcZNUl9JmnYALMBqRytJ7e/ARTVan9OJ7LOb\n7f0kn4TR9yq4MEdfj17X/P609tnvoXrYv3nQPHSfBR9fnbSveN1njgDnIOTTpqwmww+dcgQMTULy\ntdnXP6Hx03tDGiBJ66TjrPWw/aOJTozv06TQ3AMFbrXz+V4eyMK+sysSCThqFulZjizHjguZXU03\n7R/VDu3vacICuGFA0JCNqqS8rnFyK2XUxrTX+Zw6fTgBdONRHQwyNt+nk5oTghqH3QiEz9nvoUmu\n76nVaon2qJ2qaquV5JR6qs7rO3xMZJlGTYA0J6UNjVkAtXWzxHs5qa2G4gMwnzrsKzf0DPdZ0HnC\nseY9NkdAx4Cxfl89bV2Bc2eizmNtK8vR69wpSee/9o31XVjNzfKCgraaHHZs0wTNje+pSBXepxpa\nNFdg0EkUQsTpdJqwF3XTSt8g21RcagHVatUdiOFTt3WQQuT7TSeCqrDaBguWKumy0F7f7WPgPFqI\nZeA89/re5Xtf6N1Z4Jb2rH0mDzPbZxSELEPzry3Tx7BkZJKCDp9heT4/FHAOvr5+s8DOayHNSt8V\nohv3Idh8dlX9rZrJxmjj1UusEoj2WBRFLoWWfgZdG6H5Db1eD91uF1EUuZTo1dVVNJtN9Pt9dDqd\nC6FRnwSwf5V86jiQPMbLDr79TaWDvtdXvtZN+zntd1uOTyW19fEBtSUbFrblhhjVvlN/C7VdBUSo\nvQq0ep/VRm09QsxmFxD5gF77IkvLVIBV4aBtB5AwJ/RdViPxgZSlG99TkcylDi2LwD4kTCPrqNLM\nOXYqGV493xovViS3E0PVMTtR0sAgi0L3+1Dedy1EWdIxq+ys8rPeOW9ZIa3GXgsBVt7+D/VnSAsL\n3W/72SfxLcBZLVO/5xnjtLEK/abAZenGNQQFBI0kqHplY+xpkgRAwgcBIHGEuYZi7Pbeum2XxsxV\nK9F62lj/y6YQY81Sl3nrnTUGly0vr3kRumcWAAuZTrMCi312FrNO3xP6O2tdLCBZ0PLRjQICGVOZ\nlAgWQkPfRFGbDUBirbnVEOiQs4ukaFrwNBwFB/6uzzO0dNlB81Ha87P8FmLavNfztCNvfex4Zj2X\nBQhZJpItS2keDWUWSgNLXzvy1CcEmGkmopZttd4Q3TggqKdcsw/V+ZEljWznqDZATQBAgqHtenZG\nJ5j+qaaE1pHl5U30mJduQuP4MNO8oHCddci6Pg9ZrcCaKFl0o4BgPbOWyawDEUiXDHx2NBo5Bud1\ngoxuwUV/gJ5hYDfj0O+aEGIzw7SOtj55gOMqf0+T9POCWJb2kKUhpUlI3z1XoSGk9b0dm3m1sjz3\n5R3/PONk+ygPzeJDuvHEJA23qJ3js3cUFDQEyd/s4Oq+CvQJ2ChBsVhMxPx9XlyaMZqGmkdDuGkp\nf9XmzHW257La1iwawMscl6vUIrWsrDJDzu2F9iEo46u5oPsQaCeE1CH7G00GlfbMR6BWQv8AfQbM\nIOP9tp40JzTPPHR/1nX97SoBJSRhZtUQZqlTXs0kb13zviOrTWn/q2C5KrCclWbt4yyNIa0s++xV\n7Jh0LdRut136JnfrAc49/KHGhlDOaheDwSCR9qkxYUp43bQjJPUVqLiKLysnfB7Ki/55y3kV6GVo\nHTfVH1fx3qtsQ575daOAsL+/j9PTU7f0lYxrN+X0SWxV7Um08yn9eTKRmia8n1mLuvefb28AvoOA\nACQTQS4rcfPcO89kuIyGkFWer055y82jxVymnnnoZbxrVonu6wsfGOStp68MXlvYTEWSVp7qvl2N\naFW+ECDwt3a7jQ8++ABHR0duK7ByueyYnRt3bG1tuR16rPOFHaf57rw+r6p/1dJ/1gly1ZS3rbOG\nHa+iXiFGuqzJkGa6hspJA4I8lKYt5wFqnc8LqyFoApJ1JHLxh4b49DkNCZJYznA4xKNHj/Dtb38b\nOzs7OD4+dj4FAkKpVML9+/fxUz/1U3jjjTdw9+7dC/ULSVgfSC1pdsrqv6yMuzxlXBfNAwrXUQer\nzYYor+lxo4DAI8q5MzIzChnzr1arTmuwawY4IBq25JLTJ0+e4Pvf/z7+5m/+Bo8fP0a/33cagmYa\nDgYDbG9vY2NjA1tbWxeSldiBaiKk5YJf1SS4TDk+FTGvyTBPgkzee+Z9Li+FmGJW1f3DRHYu5Gnz\njQKCLi1W6a1hPsC/4kuJUpubUDx9+hTvv/8+9vb20G63EwugALituNvtNjqdDobDocs8BJJOR9bJ\nvo/ZiiE/RxqFVLyrBoLLlPUq0SySEnj12peHrCDjX50PeQD/xvMQKpWK+259A9aG1zAkswc1lHh0\ndIRHjx7h3XffxZMnT1Aul3H37l2Uy2VUKhV3WtHZ2RkODg6wvr6eOAiD/gu+iwlLfId+51Zh3GKM\ndbbts9fTVMuQTZvH3k4DBJ0IuhmHDeumSdNQXdLAzU7EPLZuVjtDxLboikrVKvOMDcuxzJVlp4fa\ncBVjacvV531rfHT1rrZHo3YLvdpRfQG6oEkb4Rs03Tt/NBrh5OQEOzs7+N73vofd3V2MRiMHBNzR\nt1qtAoA75EIPGtEOT1u0pKaK77ToPHTVGkIezcAypS/xKw/Tz1u/l0F5MvLy1sW3FmCW518WWUHg\n268DuHj4a4gWapNVuyU6U42VYblxie5m2+/3sbu7i3fffRfvvPOOO4CUOwa3Wi2sra2h0Wi48wuB\n83P3LFNn2dk2K/KqGeg6yE5unxTyPeOjWU2krHrNc0+WlF6kMcir+eSpswUqqxWoyU1+4h6RnOu1\nWi1Y/o0Cwt/93d85pqYNr4uIdDNLVXfUvo/jGL1eD8fHx3j69Kk7bowdRrOi0WhgfX3ddWYURc6x\nSI1BNRRVq4GLSU+8Jy/lURlDdJUMeFmaRRt62VL1JvvoqkAoLyjY/30aAX+jVqA7h4XoRgHhu9/9\nrtu2Wvc45JoDblCpqx+51x0ZX/dN5EaoChbUPGq1GtbX1zEYDBKbVPb7ffR6PQwGA3foqM2S9Nlt\nvJ5lF9v/55k0eSRklrT3+QuuikLJY7NK9nmv2d8vq7HN+t7LjKudP1nl+EwZzbTVPRXpZ7P3dLvd\nYPk3CghMFKKjjoxqT77R5cfcs0CRjx/VMHSX5OFw6HIauO891SvdrNQn/XWQaM5o5/vyEULMGgKP\nyyTtpJkuvufS/ARZJpBPO8jSGEL18tVjlnb7rs8CztdJ8wAK/6aZafrdN384P3VO6raC0+nzczP2\n9vaCdbnxsCOPC9eju+g30K3QSFxkZHdFIqlJoPsV2glodznO8khruRyQPMlJ12nTzlt2HsC6qrpd\nJy2ar2BemlVrs+NlhRj5hiCgR7sNBgPs7u4Gy75RQOj1ehgOh07ya+oxDwqN4/OTa+gUAZDYIp3O\nQfoFVHPggSGMNLRaLbRaLXS7XQc8LNu3kxKQ7Gg1X9IWQ+n3WaV31vW0cmdVqdOkU56yr0PSz3rN\n9/6rBre89ch7T17NzlcWn/EtsrORuvF4jG63i6OjI5ycnODk5ATtdjv4nhsFhKOjI0wmE3dWAqUv\nw4F6OIbugsR7uAiKDKp7FgDn2kStVnN5CLVazf3PsplTYE/i8XnjCQp6HPxVq6d5Jve870gzXeal\n6yjTln9dZc9Ks0rz6yYrvFRQjUYj53A/ODhwoOA7gp50o4Dw7NkzdwQ3UW11ddUthVZb3x7Nbdcy\nFItF1Go155A8PT1FsVhEpVJBo9FApVJxGgO1DwDOr0Dnpi9FWj226lTUxVc+W3pe6ZkGCGlaRNZk\nzZMgNMs1fW+eOobqnXXfPBrHVdAs2kzeusx7j++abxt3hhcHgwG63S46nY7TDLrdLk5PTxd3P4Ra\nrYZqteqkN4BE+JFLonX9gFWR7ITUU4rPzs7cGX/9fh8HBwfodDrOPFBnYtYe+UAyvHPVC5zmUddn\nKW/W5Km877gJhrwJWiTNwAfCPN6eZ4t0Oh13uCyd6nEcO34I0Y0Cwp07d9BqtQCcH5/F/RFoEsTx\n+Xn3uvLRnoDDDwDU63VUKhUXSmRa8/HxsVOjuH6BGkO5XL5wWrIvu0/XLuj5DD6aRWpmaQV57VHf\n/TZMehkTJFSvq/YhzKMZhNofopDnfh4N4DJaTJ4Qra/fVUMdjUbo9/s4OjrC4eEhjo+PE8fP05zW\njYh8dKOA0Gq1sLGxgfF4jH6/jzh+vmiI5gFXJxIUNCyoDj5fvrb9q2cp0I8AnG/VPhwOEzkMQDjM\nlmc7a6W8wBCq+2VVZtueeZk/671p91wmtJrnt3kl9yz1ui7KGhefgODu4YPBwPkJjo6O0G63HRBQ\nqFGj5TGFaXTjYUdmT2kj1W/AzrCAoKcsqRMROA8lakcQEGhOMAWaDsJer+dNY/aRL32Z1y9L1zUh\nr7rc62acRVDNr5JC4B6KaPlIo1oEg5OTExweHuLZs2fY399Hv993pjI1X5ZLU3lhfQg/+MEP0G63\nUalU3CnIutU5sxfr9bpT0bkZaqPRcGEVHsRaqVRclIGLpTqdDnZ3dxNrIk5PT9Hv91EsFrG+vo5O\np4O9vT00m020Wi23exJBShlft1IjgDHD0lLegQ49N4uGkNcMoNTQv3mfy6P6zlIXfX/a+pB5TQdf\nAk8ebSktVTytTOuQtid/aT6MarW6G7gNm6sDm9Gw4XCITqeDg4MD9Ho9d+w8Hev2iELf3A3RdoCe\n1gAAFXpJREFUjYcd33vvPayvr+O1115z4UBtCCMFut4BgFP5mXDBxnIXZQAu9bnT6aBQKDg/QbVa\nxdraGqrVKjY2NlCtVt0BsERXPZWJddFoA5BMaprVjJiXLiM57UTOqy6HfA9p2XNZpNl1FgiuwmTy\ntc/WOU+9Qr/lqUeaT8MKGTvXdA0PFyfRSdjtdtFut3F4eJjYLRxIHnvvE2ihupJuPMrQaDQco3JN\ngjr5uGkqd0+iGUFViIxIMOD1lZUV15GTyQS3bt3C/fv38eabb+Lu3buJEOXJyQmOjo4SG7Iog4cm\nll0fEKK8EnPWMuYhO/F8kjht8uYtN2+dLdOEgGee9mdpGaF32WhTnvFV5uY1jY5pNi7NV7XzGVmj\nCT0cDt2J4/wQENTB7mtvqE8XHhCazSbW1tbcgiIACX+CLussFouoVqvOCVgqlQDAhVHUeaLaQrVa\nRa/XAwCXkag7LFP9Ojo6Qq1WcwCUZmcBfpUzj9NuVhV/FpqHEfP8n6Y5ZN2TVYc0KXrVdBkQztO3\nNppj99nQKBXX3nCek8EHgwEGg4EDgE6n4xbzcR2Onl0a0lZCYJDVhhsFhI2NDfR6PYesmoIMnG+a\nQhtd05kJCGqfjUYjp/IDcIw9mUzw5MkTPH78GN///vfRbDadhkAfRLfbxcc+9jF8/OMfdwDly02w\nNiAHPkt9zjPxQ6py6Jm8qmta2b7n8kyiy4CPfY/vOymPip8Fbrb8tHstU6fVTf/nXPEteFOA4L3M\nl2HIsNPp4Pj4GHt7ey6rkI5ugontCz2C0NfWawGEKIoeAPgjAHcATAH8sziO/6coijYA/AsAbwB4\nD8AX4jg+efHM2wC+BGAM4FfjOP6Gr+zbt28nwiGadUhJTamvDVeU5VFsZ2dnGAwGGA6HiKLnuyoz\n6alUKmF/fx97e3sOfOxSa+C5o/LevXuo1+sOVEJhRt23IcSsIdV7XgoxSkjVnrf865bU87xjXifg\nyyK+327Cy0gYcB76Vrtf1xlo6HAwGCCOz08c060CmWSk6xa0Dj4TIe/Y5tEQxgD+qziOvxNFUQPA\n/xtF0TcA/GcAvhnH8e9EUfQbAN4G8OUoiv4egC8A+CSABwC+GUXRJ2JPTe7evYvV1VW3ixHXE1Ab\noB+BQKFqFztb9ziM4+fhSZoWpVLJJSmNRiM8ffoUx8fHztnI95RKJVQqFbz++uuJ5ChdTq2dqeqe\nqoZ20voGKG1AsjSEPJLW93zoPb575mUs1ZxmeSb0vrzhuJsCAl8fU1MlIKhfIIoiF/ajI/zs7Ay7\nu7vY2dlxqcWDwcClFzP6Rt8Y/Qo0G0L7dlxGQGQCQhzHTwE8ffG9G0XRv8NzRv9FAD//4rY/BPAX\nAL4M4B8C+JM4jscA3oui6B0AnwHwV7bszc1Nx6zMwbY7J2kegfUrAHDHwDEESeauVqvOwcg8g9Fo\nhIODA3S7Xad1EDgYsjw7O8PZ2Znz8NJEsdJZMxbtqsdFBoR5VfxZk5ou85zVfnw0z2TPCidmUUjS\n6hywXn6Gzzk/T09PcXJygoODA+zv7+P4+Bjtdtv5EOxO5DRDKCyZ0u/b2Ddt3DXUbJ2RSjP5EKIo\nehPApwD8PwDuxHG8+6ICT6Mouv3itvsA/lIe23lx7QJxn0PLhLqem1uc0aHC68wBINNSxScI6EYq\nAJyHVtdI0LtLQCiVSq4eRHmLwi/6wQ28PfjV0mXV+dDzN60iv4qU5gi+LHGMrG+JDmyq+t1uF7u7\nu3j69Cn29vbQ6/Wc05zCjWYG62ZX1nKrAEYp9P1Z7b8Kk4GFNQD8H3juE+hGUWRLnnmGUvVmMpGq\n5NzvkOEZ1QpUSzg7O3NMXSgUUKlUMBwOEw4cql48ss0yMtOk7cYSRFMdDA6SbsOmUgDwO6Ty2HDz\naha23Fk0BN+EmkeLCZUfKjuPWZOnHnnoKtqYZd5w/ulmpgDclmUnJyd4+vQpnj17hna77czWUqmU\nEGrMntVl/6FduWwUI6/2l3ZfLkCIoqiI52Dwv8Vx/GcvLu9GUXQnjuPdKIruAuC+TDsAHsrjD15c\nu0C///u/7xjrZ3/2Z/HZz37WISPVK12voPaYJm2w06huRVHk1iYQoVdXV12YUx2ZBINGo+EiF9p5\nIUDg7/R5KGDlmXRZNCsIhN6b19TwAUro/9C70uo4CzDmpcswQFqf2O95NDT1J3F+ccERU4v39vbc\nHiBRFKFSqSR2Grep9zq3LfnM1LT+YDgzre1Afg3hfwXw3TiO/0e59ucAvgjgqwB+CcCfyfU/jqLo\n9/DcVPg4gG/5Cv2VX/mVhKdeHSW607JlSF2VeHZ25kCDzkQeEUcnDVM5W60WptOpA4U4jt1v3FnJ\nOhLt+7UzOfC082w0Io3J0u7xMU/eye8rMw+zZ036WUAiT91CwJDHfxCq72XrmtX+0DM0XyeTiTse\ngIcBPXr0CLu7uzg4OHDP88Ag+rDop6I2zLI439V8YD1mBYRqteqS++I4xsnJife+PGHHnwPwnwD4\n/6Io+jaemwa/iedA8KdRFH0JwA/xPLKA/7+9a4mN7Kii5zpjj38ztkk0iUwgCcoisIpYsAnsUBSx\nIIhVdoDEChBILEiyyjKwYMGGDR8pIKEIsUiyIyBWII0SKTNMYGY8Hs0EM8Y27bbHsdsfeUix6Hdq\nbl9X1Xuv/XntqI7U6u569d679Tt169atKufcVRH5PYCrAPYBfMdVqM2Monvas2fP+gbPTC9k8vfo\nwnDOeS2BmgG/ucqRxMH7te84hw3aoBmTVb87ZHhsGlW0grLwsgYRG3/bxm7j9aMpVGnk/cTvR5uy\n0H4zGxsb6HQ63mi4urqKTqcDkYO7fPFevYxZDxV0/mkj9mHqWpnNpMosw98AxNz2vhy551UAr5Y9\nWyeyuA8AvEo1NjbmGxwAv7bbGm706kbt/qxXT3Irdg4R+D5tsNGEUMXhQ5PRYTdL6ffeKipu1Z6u\n6n1ViKCsYcUqtnXwKtMY+iG3WDjLtCxeCHQ5brfbWFxcxNLSEjY3N73WMDU1FV3dy+ErP6zbVkvQ\nmqvVGsr8NGy+RtORTOUJgAnQ6jbHVc45v9qRVn/O5eqGzPHW9va2tyXs7+/7oQNdlukQQqck51zP\nIiZuzKIXUumVaATHeyQe7ZNgHVF0AaUqty1c3RvYwtbGTh3XfjOubiDWmSrU6PUzYhXMpiukvoZk\nCaXXxks906IKuZX17PZ9Ns8t2XPIysbc6XSwurrqd+TiBjyMy/LSNi0+Vy9i0h2cNRgC6Clzmydl\neXVqCAE4uCxVaw5cnUir/9bWVs9hFAB8ZjvnelyXqSVQ/efwA4DXOthAaEfQBkL6N+jCYWaSEDgF\nZJethipS1fTrQo+RiG7YoQLW95f1/PxOxQv11jENItb7p2SLTYlZQ21V1CEE+23ToWedtIq/t7eH\nDz/8EO12G7du3cKdO3d8Z8FvPlNvvmMNkJoQUunhd0hem96Ur8HA7oegEWIw3ety9yS9BNk6LLFS\nMZw2BzZszt3quCQM+hWEdmHSlVXvhaAbv93GDahn+LLxYw2T0AWeqvixRh7TDGKw1+xzqvTmQHzl\nqP628th7UkTCuKHhaEjrst96TQ3Lkv4BLGsuiFtbW/OaQafT8bNUmjxDxr+Yh6uVM5VHoQ5D3281\nQR1/4DWEEHRi6GfAcP2t10FwsYe2K9ARyfo76IziNc3u+h1V1C3bk+j7U2kLpSkUP5VPZWF13pVC\n6LlVwvpBKs9TFVpX+FDDso1Tk3/onXp4SC1za2sLa2trWFlZQavVwt27d72dSr9P1zWt6dhPnTSH\nhjk2XqrenlpCAO73glTntXVWZ7R2VNKqGh2aqC3o3ZaBXjLgdJFehm0L0cqmw/W4L6a61SUKHd5v\nmA2PEUKscZe9o4pWUCZTLEw31JjGELM9hCq+rjMxMmA4ZwQI2go4ROBGpjzwh3WJz7C2gJQKb99t\n8yKlDcSeYZ/zsSAEQhcaz4PUFYQLRXSDpJGRGoRW51ng1tvQagchQqAWYg2GMUKIoU6Drovj0BDq\nkNVRom6vaeWK+fzH5Na2IMblTt2WCOjjom0LlgxSdqSYVmKHEaF8CJFgKo9i/y0GmhBsJrAhA73M\nx8KjfYBkwK2o9/b2fFy98aS1FtOxidCkoDUQXfjaCGm3U4v1ahqhxpqKUxa3yvW6jTuk3vaLoxoW\nxbQtK6tuXNbrNPQeu/kIhwitVgvLy8tot9vY2dnxHYPWRkOzBaE6YG0GNh39aloxe4q9dmo1BK0q\ncmxO9ubCJe03QF9yO/vARVEAeg5ooceYNiZq/3GtBobYnF5mdvFJiOEHBUfVsE8KZQRShRDqDGtY\nB3Z3d/1mvOvr61hfX8f29rYnAj2TRDKgn0yMdFIk3C8hhOSP/bdDrxAGmhDsYiOCjdFO7fBbTy8S\ndldbfaQb/Q/o38AGTkKg5dkOE6yxKSQncPxqdV0cRrsoQ8jQlXpmXc1AX4u9xw73Qj1y7D/l73Q6\nfhcjHpCq99HQM1bWNlU1/2waytIbswtY+VPpO7WEYA0jWmXnh7MP1nGI27efO3fO78KsnZOYuRwq\ncIZCr5/Q69G5YIr+DFU+ddc1HJY0qtoJjkJDiFnvQ+/s53399qxWxhRsGnSvzmECT/vSG5tqDZK/\nQ9oj65jVTuoMf8rSlLKhaITSGcPAEgKAYIbqaT1qCnRAYry9vT0MDw/7sxtGRkb8ga5ketoBOLxw\n7r6jk96fQW/9TmhZNFnZMWMIx6EtnNQwQOd7lfcdhUz9pi3WSGIzD1r9393d9Vudb25uejKwH96n\nfVdC7wuRgu7lYzsfxZ6TMj7atNnnleXlQBOCzbSY6sdpSYI9NBcdcf8DfS+v663SaFDkDAQriNVW\nNBkwzK5dDxW+lVv/r0sidhqrqtZxXI207hi4jmwpAgpds/UkNizgh0PB/f19f1ry5uam36WIHQeX\nNOshpZ6tsnmQMmKGiKIsH0KwpBAzWOpnnlqjIhOhySDU2NjAqdrrjKDVmMMMnm4zMTHh96uzzwZ6\n11PozU8IOqHQlqE1hX4IIZT2OiRxXI2/6ju0vKHK36+RtcrsTMi6HppZsPfrMqMhkQZEDhO0j0to\n1kD/1z19isB4vWy8b6/FOkQrjw3/WBCCzWg93WedkQi6H/N+reqx8U5OTmJ4eBgPPvggzp8/j/Hx\ncXz0Ue/5j3o5NYCeYYqWhR+7Wk3PiFAWnS6bzrL0x66XPecwJFHFOGhJIFYBY6tH6yDWkGLkY+OE\njL3aae3evXueENrtds9ZCSQCdih6IxO9W5buGGLy299l5ZxC2X0x7SSFgSUEIDxNwv8sdBaCrgS0\nKXBBydbWFoD7PukTExN46KGHMDExgaGhIb9fI20SuufgO+3uyyGHE10ZOOTQ7G3TM2hTk/1UzLIx\n7FGksQr5hd6jXcm1xqfrEHB/E14aELkFn+58rOE6lVcxUgppBNYuE9NmyvIkhao+CMAAE4IV3DZA\n7SNgeyaukATgd06i9jA6Oorz589jenoaIyMj2N3d9Ztf0q+AFYHzynYXaDsFad8fsjnYWYo6DaVu\nQ62qhaTuLavwTFOq0YeGCimNKBZeR1PSYF7rGSPnXM8J40CXELhYiU5Hupx1edtPqB4y7SzvUGO3\n9YJhIe9Y+x2bXUgRZdX6djInlEZw8eLF0jiphKQyRqv/bPBra2tYXl7G4uIiFhcXsbq6ip2dHZw5\ncwYzMzOYnp7G5OQkxsbG/HkN9GmPbXw5NDSEubm5oI3DIlZgdceSVRt4nbjXrl2rFC9UHlXTVRf9\n5MPc3FxPw2Jj1MMDvR8Bp56556A2PofeF/ro69aGlJoq1FPoWuOl1qrjhrSOKjLWxcATQj/QDZdH\naG9sbKDVamFhYQE3b97E/Pw8bt++jZWVFX/2I3sPnhOht4W3FUtrAtevX/fvThVEPz31cSBUqaoS\nwkmh30p948aNAw2StidNBpoQeEgQjYh1ZEuRhIVt4DHQLmWJoCoxlMmawsAOGQ4DbYDkJhYrKytY\nWlpCq9XC9vY2xsbGMD4+jrNnz/qDXfTahgceeABTU1O4cOECZmdnMT4+3nO8G4ADFY+IVZKy3jWm\nItbRCOpcixkOq9xfd1hyFCRZBbbi67KywzXdYXCXbksYqSGDfV8qT+oMs/htDZEpQ2ksL2KyxPCx\nJATgfgHwzEcepLmwsIB2u+0NiNyleXR01E8hAt2j6mdnZzE0NISZmRmMjo72+CqkKp6tHHXV/OOC\nrohlFfI45TgJUH47JUybAuNo1+PQ5jZ1tYAULBnEhhO2TvH3cQ7B/HuaKng5eNBLRkbGCcI5d4Bh\nGiOEjIyMwUOjRsWMjIzBQiaEjIwMj0YIQUSeE5HrInJDRF5sQoZ+ISIfiMjfReSSiLxThM2IyNsi\nMicifxSRqabltBCRX4nIiohcUWFRuUXkZRGZF5FrIvJsM1IfRCQdr4jIHRF5r/g8p64NXDpE5FER\n+YuI/FNE3heR7xfhzZdHFWvqUX7QJaGbAB4DMAzgMoCnTlqOQ8h/C8CMCfsJgB8Vv18E8OOm5QzI\n/UUATwO4UiY3gM8BuITuLNTjRXlJ02lIpOMVAD8MxP3sIKYDwCMAni5+TwKYA/DUIJRHExrCFwDM\nO+f+5ZzbB/A6gOcbkKNfCA5qVs8DeK34/RqAr52oRBXgnPsrgHUTHJP7qwBed87dc859AGAe3XJr\nHJF0AN1ysXgeA5gO59yyc+5y8XsLwDV0T0lvvDyaIIRPAvi3+n+nCDstcAD+JCLvisi3i7CHnXMr\nQLewAVxoTLp6uBCR25bRIga/jL4nIpdF5JdK1R74dIjI4+hqPBcRr0cnlo5sVKyPZ5xznwfwFQDf\nFZEvoUsSGqd1Lve0yv1zAJ9xzj0NYBnATxuWpxJEZBLAHwD8oNAUGq9HTRDCIoBPq/+PFmGnAs65\npeK7BeANdFW3FRF5GABE5BEA/21OwlqIyb0I4FMq3kCXkXOu5YrBNoBf4L46PbDpEJEz6JLBb51z\nbxbBjZdHE4TwLoAnReQxERkB8AKAtxqQozZEZLxgdYjIBIBnAbyPrvzfLKJ9A8CbwQc0D0HvWDsm\n91sAXhCRERF5AsCTAN45KSEroCcdReMhvg7gH8XvQU7HrwFcdc79TIU1Xx4NWVmfQ9eyOg/gpaat\nvjXkfgLdWZFL6BLBS0X4JwD8uUjT2wCmm5Y1IPvvAPwHwB6ABQDfAjATkxvAy+has68BeLZp+UvS\n8RsAV4qyeQPdsfjApgPAMwD+p+rSe0WbiNajk0pHdl3OyMjwyEbFjIwMj0wIGRkZHpkQMjIyPDIh\nZGRkeGRCyMjI8MiEkJGR4ZEJISMjwyMTQkZGhsf/AXR+b0dfzObsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ee02c02b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(roi_t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26086479"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_t0.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1ee0134438>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EN3o9Fzl92j5eB5Qy4Mnk1OIL9c/ELzbYzl7X/0kupwHPsvglq4nwLC3+zV3p/JufVyoV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\nnlxyyWXtkgNPLrnksnb5/8JgA6J5ApzEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ee01eb550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skimage.io.imshow(vgg.img_porcessed.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.report_uninitialized_variables())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/anaconda3/lib/python3.5/site-packages/skimage/io/_plugins/matplotlib_plugin.py:77: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
      "  warn(\"Float image out of standard range; displaying \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1ee012b2b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ee01efa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feed_dict = {vgg.imgs: [roi_t0]}\n",
    "res4, res5 = sess.run([vgg.conv4_3, vgg.conv5_3], feed_dict=feed_dict)\n",
    "skimage.io.imshow(res4[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.misc import imresize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train the local-SelCNN network for 600 times.\n",
      "0.0013186425201 max of mask\n",
      "(224, 224, 3)\n",
      "250 max convas\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/repos/FCNT_bak/inputproducer.py:85: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  roi = convas[cy-half:cy+half, cx-half:cx+half, :]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "Step: 0  , sel_local Learning rate: 1e-06   Loss: 36.53  \n",
      "Step: 20  , sel_local Learning rate: 9.72655e-07   Loss: 13.24  \n",
      "Step: 40  , sel_local Learning rate: 9.46058e-07   Loss: 6.24  \n",
      "Step: 60  , sel_local Learning rate: 9.20188e-07   Loss: 3.81  \n",
      "Step: 80  , sel_local Learning rate: 8.95025e-07   Loss: 2.39  \n",
      "Step: 100  , sel_local Learning rate: 8.70551e-07   Loss: 1.92  \n",
      "Step: 120  , sel_local Learning rate: 8.46745e-07   Loss: 1.67  \n",
      "Step: 140  , sel_local Learning rate: 8.23591e-07   Loss: 1.26  \n",
      "Step: 160  , sel_local Learning rate: 8.0107e-07   Loss: 1.02  \n",
      "Step: 180  , sel_local Learning rate: 7.79165e-07   Loss: 0.78  \n",
      "Step: 200  , sel_local Learning rate: 7.57858e-07   Loss: 0.70  \n",
      "Step: 220  , sel_local Learning rate: 7.37135e-07   Loss: 0.61  \n",
      "Step: 240  , sel_local Learning rate: 7.16978e-07   Loss: 0.53  \n",
      "Step: 260  , sel_local Learning rate: 6.97372e-07   Loss: 0.46  \n",
      "Step: 280  , sel_local Learning rate: 6.78302e-07   Loss: 0.43  \n",
      "Step: 300  , sel_local Learning rate: 6.59754e-07   Loss: 0.36  \n",
      "Step: 320  , sel_local Learning rate: 6.41713e-07   Loss: 0.30  \n",
      "Step: 340  , sel_local Learning rate: 6.24165e-07   Loss: 0.32  \n",
      "Step: 360  , sel_local Learning rate: 6.07097e-07   Loss: 0.25  \n",
      "Step: 380  , sel_local Learning rate: 5.90496e-07   Loss: 0.26  \n",
      "Step: 400  , sel_local Learning rate: 5.74349e-07   Loss: 0.26  \n",
      "Step: 420  , sel_local Learning rate: 5.58644e-07   Loss: 0.21  \n",
      "Step: 440  , sel_local Learning rate: 5.43367e-07   Loss: 0.21  \n",
      "Step: 460  , sel_local Learning rate: 5.28509e-07   Loss: 0.20  \n",
      "Step: 480  , sel_local Learning rate: 5.14057e-07   Loss: 0.20  \n",
      "Step: 500  , sel_local Learning rate: 5e-07   Loss: 0.23  \n",
      "Step: 520  , sel_local Learning rate: 4.86327e-07   Loss: 0.18  \n",
      "Step: 540  , sel_local Learning rate: 4.73029e-07   Loss: 0.16  \n",
      "Step: 560  , sel_local Learning rate: 4.60094e-07   Loss: 0.16  \n",
      "Step: 580  , sel_local Learning rate: 4.47513e-07   Loss: 0.14  \n",
      "Training the local-SelCNN cost 11.15 s\n"
     ]
    }
   ],
   "source": [
    "# Gen anotated mask for target arear\n",
    "print('Train the local-SelCNN network for %s times.'%FLAGS.iter_step_sel)\n",
    "t = time.time()\n",
    "lselCNN = SelCNN('sel_local', vgg.conv4_3, (1,28,28,1))\n",
    "sgt_M = inputProducer.gen_mask(lselCNN.pre_M_size)\n",
    "sgt_M = sgt_M[np.newaxis,:,:,np.newaxis]\n",
    "feed_dict = {vgg.imgs: [roi_t0], lselCNN.gt_M: sgt_M}\n",
    "loss, pm = train_selCNN(sess, lselCNN, feed_dict)\n",
    "print('Training the local-SelCNN cost %.2f s'%(time.time() - t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/anaconda3/lib/python3.5/site-packages/skimage/io/_plugins/matplotlib_plugin.py:77: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
      "  warn(\"Float image out of standard range; displaying \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e944a8c18>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ee004a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res4, res5 = sess.run([vgg.conv4_3, vgg.conv5_3], feed_dict=feed_dict)\n",
    "skimage.io.imshow(res4[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xlws/anaconda3/lib/python3.5/site-packages/skimage/io/_plugins/matplotlib_plugin.py:77: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
      "  warn(\"Float image out of standard range; displaying \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e94730630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nrEi6BPgocJmktZJ+JOnKSa6S7m1E1ECfY3q2v0/r9RSdzjlnOm0l6UVE5XJHRkQ0S5Je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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ee011ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skimage.io.imshow(res5[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e946737b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARwAAAEZCAYAAABFOZpTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e9476f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "skimage.io.imshow(sgt_M[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1e945cfe48>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X0FDeMomIVII+He4jRsDYsbBnDxx9dIxTuIuIdK1Ph/shh8Dw4fuO27ixPGUREakkfTrc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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e9468c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9OE1v5ZMA37Y+AtYXZBs/GZNrbO4jSQ9V89gAs78xbd7G/m39e8gSfqJH0FVb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\nKslleANXjT4NzdgYp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Rtgu7aRLXWG01AG22+1LUXL+I2GDFvGV6R1Yx4alTMRw1d8WxGv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\nWUQ+zbo8aJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e945e2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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msoERgHg992fQztzlY7px9K9qweP2mmd/jMtdCcRVZP3Y3673+VbxjlfEKWHu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\nPqT/D1OIpizp99KFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e94576828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iy1Be+Wv6VrE6Aq49BaCfUYNeA7+2xBr4mu1AiQfUYK6tvbClW0yn1LPAt05P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e9440acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img in pm:\n",
    "    plt.figure()\n",
    "    plt.imshow(img[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e943608d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ONIHgaikPffyvN1G/uHATA9uBnIB7nKUrlKexBdiI2lwbxPxU7dkScMyiz9t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\nE98wzhAT3zDOkP8fjWzmhOuVclUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e945e2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(sgt_M[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e942c2400>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e9436f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(sgt_M[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train the global-SelCNN network for 600 times.\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Tensor(\"sel_global/kernel:0\", shape=(3, 3, 512, 1), dtype=float32_ref, device=/device:CPU:0) must be from the same graph as Tensor(\"dropout/mul:0\", shape=(?, 14, 14, 512), dtype=float32).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-cb5a4861431e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Train the global-SelCNN network for %s times.'\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mFLAGS\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_step_sel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mgselCNN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSelCNN\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sel_global'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv5_3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mgselCNN\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'lr_initial'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-5ef4193319ce>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, scope, vgg_conv_layer, gt_M_sz)\u001b[0m\n\u001b[0;32m     35\u001b[0m \t\t}\n\u001b[0;32m     36\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mscope\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpre_M\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_pre_M\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     38\u001b[0m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgt_M\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgt_M_sz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpre_M_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpre_M\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-5ef4193319ce>\u001b[0m in \u001b[0;36m_get_pre_M\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     48\u001b[0m                 \u001b[1;31m# Conv layer with bias\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mkernel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvariable_with_weight_decay\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'kernel'\u001b[0m\u001b[1;33m,\u001b[0m                                                       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'k_size'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'wd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m                 \u001b[0mconv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdropout_layer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'SAME'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m                 \u001b[0mbias\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvariable_on_cpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'biases'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstant_initializer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m                 \u001b[0mpre_M\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/xlws/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[1;34m(input, filter, strides, padding, use_cudnn_on_gpu, data_format, name)\u001b[0m\n\u001b[0;32m    392\u001b[0m                                 \u001b[0mstrides\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpadding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m                                 \u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 394\u001b[1;33m                                 data_format=data_format, name=name)\n\u001b[0m\u001b[0;32m    395\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    396\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/xlws/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36mapply_op\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    328\u001b[0m       \u001b[1;31m# Need to flatten all the arguments into a list.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m       \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 330\u001b[1;33m       \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_graph_from_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_Flatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeywords\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    331\u001b[0m       \u001b[1;31m# pyline: enable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    332\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mAssertionError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/xlws/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_get_graph_from_inputs\u001b[1;34m(op_input_list, graph)\u001b[0m\n\u001b[0;32m   3814\u001b[0m         \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_element\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3815\u001b[0m       \u001b[1;32melif\u001b[0m \u001b[0moriginal_graph_element\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3816\u001b[1;33m         \u001b[0m_assert_same_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_graph_element\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgraph_element\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3817\u001b[0m       \u001b[1;32melif\u001b[0m \u001b[0mgraph_element\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgraph\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3818\u001b[0m         raise ValueError(\n",
      "\u001b[1;32m/home/xlws/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_assert_same_graph\u001b[1;34m(original_item, item)\u001b[0m\n\u001b[0;32m   3759\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0moriginal_item\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3760\u001b[0m     raise ValueError(\n\u001b[1;32m-> 3761\u001b[1;33m         \"%s must be from the same graph as %s.\" % (item, original_item))\n\u001b[0m\u001b[0;32m   3762\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3763\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Tensor(\"sel_global/kernel:0\", shape=(3, 3, 512, 1), dtype=float32_ref, device=/device:CPU:0) must be from the same graph as Tensor(\"dropout/mul:0\", shape=(?, 14, 14, 512), dtype=float32)."
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print('Train the global-SelCNN network for %s times.'%FLAGS.iter_step_sel)\n",
    "t = time.time()\n",
    "gselCNN = SelCNN('sel_global', vgg.conv5_3, (1,14,14,1))\n",
    "gselCNN.params['lr_initial'] = 1e-10\n",
    "\n",
    "ggt_M = inputProducer.gen_mask(gselCNN.pre_M_size)\n",
    "ggt_M = ggt_M[np.newaxis,:,:,np.newaxis]\n",
    "feed_dict[gselCNN.gt_M] = ggt_M # corrpus the other nets?\n",
    "loss, pm = train_selCNN(sess, gselCNN, feed_dict)\n",
    "print('Training the global-SelCNN cost %.2f s'%(time.time() - t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "show(ggt_M[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for img in pm:\n",
    "    plt.figure()\n",
    "    plt.imshow(img[0,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print('Performing local and gloal SelCNN feature map selection.')\n",
    "t = time.time()\n",
    "s_sel_maps, s_idx, sgs = lselCNN.sel_feature_maps(sess, vgg.conv4_3, feed_dict,318)\n",
    "#g_sel_maps, g_idx, sgg = gselCNN.sel_feature_maps(sess, vgg.conv5_3, feed_dict,FLAGS.num_sel)\n",
    "print('Sel-CNN selection porcesses cost : %.2f s'%(time.time() - t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_sel_sum = s_sel_maps.sum(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_sel_sum = s_sel_sum.sum(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_sel_sum.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_sel_avg = (s_sel_sum / 318)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "show(s_sel_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "original_maps = s_sel_maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "original_maps.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res4.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "show((original_maps.sum(axis=0).sum(axis=-1)/318))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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